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Pyarilal S, Sivakumar A, Anantharaju A, Krishnamurthy A, Pal UM. Early detection of carcinoma: correlating quantifiable tumor biomarkers with High-Resolution Microscopy (HRME) findings. Expert Rev Mol Diagn 2025; 25:33-45. [PMID: 39778093 DOI: 10.1080/14737159.2025.2451717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2024] [Revised: 12/22/2024] [Accepted: 01/07/2025] [Indexed: 01/11/2025]
Abstract
INTRODUCTION Cancer ranks as the second most prevalent cause of death worldwide, responsible for approximately 9.6 million deaths annually. Approximately one out of every six deaths is caused by cancer. About 80% of cancer deals with epithelial tissues located on the outer lines of the body cavity. AREAS COVERED This review study selected and analyzed recent works in the field of High Resolution Microendoscopy (HRME) that have been used to diagnose cancer in various organs such as cervical, esophageal, head & neck, and gastrointestinal. EXPERT OPINION The HRME modality will play a vital role in improving the diagnostic accuracy of carcinoma. HRME has shown promising statistical outcomes for diagnosing carcinoma, enabling the clinician to gain additional information before performing conventional tissue biopsy. A multimodal probe consisting of a macroscopic investigation aided by HRME modality for microscopic investigation can significantly reduce the number of unnecessary biopsies leading to overall improvement in patient wellness. The new directions of the HRME research would be in the light source and detection configuration, increasing the number of optical fiber cores, which improves the resolution of the image, AI-assisted automatic quantification of the key HRME parameters, and clinical studies with newer near-infrared regime-based contrast agents.
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Affiliation(s)
- Sreelakshmi Pyarilal
- The School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Scotland, UK
| | - Aathira Sivakumar
- Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, India
| | | | | | - Uttam M Pal
- Department of Electronics and Communication Engineering, IIITDM Kancheepuram, Chennai, India
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Richards-Kortum R, Lorenzoni C, Bagnato VS, Schmeler K. Optical imaging for screening and early cancer diagnosis in low-resource settings. NATURE REVIEWS BIOENGINEERING 2024; 2:25-43. [PMID: 39301200 PMCID: PMC11412616 DOI: 10.1038/s44222-023-00135-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/05/2023] [Indexed: 09/22/2024]
Abstract
Low-cost optical imaging technologies have the potential to reduce inequalities in healthcare by improving the detection of pre-cancer or early cancer and enabling more effective and less invasive treatment. In this Review, we summarise technologies for in vivo widefield, multi-spectral, endoscopic, and high-resolution optical imaging that could offer affordable approaches to improve cancer screening and early detection at the point-of-care. Additionally, we discuss approaches to slide-free microscopy, including confocal imaging, lightsheet microscopy, and phase modulation techniques that can reduce the infrastructure and expertise needed for definitive cancer diagnosis. We also evaluate how machine learning-based algorithms can improve the accuracy and accessibility of optical imaging systems and provide real-time image analysis. To achieve the potential of optical technologies, developers must ensure that devices are easy to use; the optical technologies must be evaluated in multi-institutional, prospective clinical tests in the intended setting; and the barriers to commercial scale-up in under-resourced markets must be overcome. Therefore, test developers should view the production of simple and effective diagnostic tools that are accessible and affordable for all countries and settings as a central goal of their profession.
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Affiliation(s)
- Rebecca Richards-Kortum
- Department of Bioengineering, Rice University, Houston, TX, USA
- Institute for Global Health Technologies, Rice University, Houston, TX, USA
| | - Cesaltina Lorenzoni
- National Cancer Control Program, Ministry of Health, Maputo, Mozambique
- Department of Pathology, Universidade Eduardo Mondlane (UEM), Maputo, Mozambique
- Maputo Central Hospital, Maputo, Mozambique
| | - Vanderlei S Bagnato
- São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Kathleen Schmeler
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
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Guidozzi N, Menon N, Chidambaram S, Markar SR. The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis. Dis Esophagus 2023; 36:doad048. [PMID: 37480192 PMCID: PMC10789250 DOI: 10.1093/dote/doad048] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
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Affiliation(s)
- Nadia Guidozzi
- Department of General Surgery, University of Witwatersrand, Johannesburg, South Africa
| | - Nainika Menon
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
| | - Swathikan Chidambaram
- Academic Surgical Unit, Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London, UK
| | - Sheraz Rehan Markar
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, UK
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Brenes D, Kortum A, Carns J, Mutetwa T, Schwarz R, Liu Y, Sigel K, Richards-Kortum R, Anandasabapathy S, Gaisa M, Chiao E. Automated In Vivo High-Resolution Imaging to Detect Human Papillomavirus-Associated Anal Precancer in Persons Living With HIV. Clin Transl Gastroenterol 2023; 14:e00558. [PMID: 36729506 PMCID: PMC9944690 DOI: 10.14309/ctg.0000000000000558] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 11/22/2022] [Indexed: 02/03/2023] Open
Abstract
INTRODUCTION In the United States, the effectiveness of anal cancer screening programs has been limited by a lack of trained professionals proficient in high-resolution anoscopy (HRA) and a high patient lost-to-follow-up rate between diagnosis and treatment. Simplifying anal intraepithelial neoplasia grade 2 or more severe (AIN 2+) detection could radically improve the access and efficiency of anal cancer prevention. Novel optical imaging providing point-of-care diagnoses could substantially improve existing HRA and histology-based diagnosis. This work aims to demonstrate the potential of high-resolution microendoscopy (HRME) coupled with a novel machine learning algorithm for the automated, in vivo diagnosis of anal precancer. METHODS The HRME, a fiber-optic fluorescence microscope, was used to capture real-time images of anal squamous epithelial nuclei. Nuclear staining is achieved using 0.01% wt/vol proflavine, a topical contrast agent. HRME images were analyzed by a multitask deep learning network (MTN) that computed the probability of AIN 2+ for each HRME image. RESULTS The study accrued data from 77 people living with HIV. The MTN achieved an area under the receiver operating curve of 0.84 for detection of AIN 2+. At the AIN 2+ probability cutoff of 0.212, the MTN achieved comparable performance to expert HRA impression with a sensitivity of 0.92 ( P = 0.68) and specificity of 0.60 ( P = 0.48) when using histopathology as the gold standard. DISCUSSION When used in combination with HRA, this system could facilitate more selective biopsies and promote same-day AIN2+ treatment options by enabling real-time diagnosis.
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Affiliation(s)
- David Brenes
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Alex Kortum
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Jennifer Carns
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Tinaye Mutetwa
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Richard Schwarz
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Yuxin Liu
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Keith Sigel
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Michael Gaisa
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Elizabeth Chiao
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of General Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Messmann H, Bisschops R, Antonelli G, Libânio D, Sinonquel P, Abdelrahim M, Ahmad OF, Areia M, Bergman JJGHM, Bhandari P, Boskoski I, Dekker E, Domagk D, Ebigbo A, Eelbode T, Eliakim R, Häfner M, Haidry RJ, Jover R, Kaminski MF, Kuvaev R, Mori Y, Palazzo M, Repici A, Rondonotti E, Rutter MD, Saito Y, Sharma P, Spada C, Spadaccini M, Veitch A, Gralnek IM, Hassan C, Dinis-Ribeiro M. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54:1211-1231. [PMID: 36270318 DOI: 10.1055/a-1950-5694] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings. MAIN RECOMMENDATIONS:: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett's high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett's neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.
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Affiliation(s)
- Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
| | - Diogo Libânio
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pieter Sinonquel
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Mohamed Abdelrahim
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
| | | | - Pradeep Bhandari
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dirk Domagk
- Department of Medicine I, Josephs-Hospital Warendorf, Academic Teaching Hospital, University of Muenster, Warendorf, Germany
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Tom Eelbode
- Department of Electrical Engineering (ESAT/PSI), Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center Tel Hashomer & Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel
| | - Michael Häfner
- 2nd Medical Department, Barmherzige Schwestern Krankenhaus, Vienna, Austria
| | - Rehan J Haidry
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
| | - Rodrigo Jover
- Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain
| | - Michal F Kaminski
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland
- Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Roman Kuvaev
- Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
- Department of Gastroenterology, Faculty of Additional Professional Education, N.A. Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Matthew D Rutter
- North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, UK
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Prateek Sharma
- Gastroenterology and Hepatology Division, University of Kansas School of Medicine, Kansas, USA
- Kansas City VA Medical Center, Kansas City, USA
| | - Cristiano Spada
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Digestive Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Andrew Veitch
- Department of Gastroenterology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
| | - Ian M Gralnek
- Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel
- Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
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Brenes DR, Nipper AJ, Tan MT, Gleber-Netto FO, Schwarz RA, Pickering CR, Williams MD, Vigneswaran N, Gillenwater AM, Sikora AG, Richards-Kortum RR. Mildly dysplastic oral lesions with optically-detectable abnormalities share genetic similarities with severely dysplastic lesions. Oral Oncol 2022; 135:106232. [PMID: 36335817 PMCID: PMC9881670 DOI: 10.1016/j.oraloncology.2022.106232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Optical imaging studies of oral premalignant lesions have shown that optical markers, including loss of autofluorescence and altered morphology of epithelial cell nuclei, are predictive of high-grade pathology. While these optical markers are consistently positive in lesions with moderate/severe dysplasia or cancer, they are positive only in a subset of lesions with mild dysplasia. This study compared the gene expression profiles of lesions with mild dysplasia (stratified by optical marker status) to lesions with severe dysplasia and without dysplasia. MATERIALS AND METHODS Forty oral lesions imaged in patients undergoing oral surgery were analyzed: nine without dysplasia, nine with severe dysplasia, and 22 with mild dysplasia. Samples were submitted for high throughput gene expression analysis. RESULTS The analysis revealed 116 genes differentially expressed among sites without dysplasia and sites with severe dysplasia; 50 were correlated with an optical marker quantifying altered nuclear morphology. Ten of 11 sites with mild dysplasia and positive optical markers (91%) had gene expression similar to sites with severe dysplasia. Nine of 11 sites with mild dysplasia and negative optical markers (82%) had similar gene expression as sites without dysplasia. CONCLUSION This study suggests that optical imaging may help identify patients with mild dysplasia who require more intensive clinical follow-up. If validated, this would represent a significant advance in patient care for patients with oral premalignant lesions.
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Affiliation(s)
- David R. Brenes
- Rice University, Department of Bioengineering MS-142, 6100 Main St., Houston, TX 77005, USA
| | - Allison J. Nipper
- The University of Texas MD Anderson Cancer Center, Department of Head & Neck Surgery, 1400 Pressler Street, Houston, TX 77030, USA
| | - Melody T. Tan
- Rice University, Department of Bioengineering MS-142, 6100 Main St., Houston, TX 77005, USA
| | - Frederico O. Gleber-Netto
- The University of Texas MD Anderson Cancer Center, Department of Head & Neck Surgery, 1400 Pressler Street, Houston, TX 77030, USA
| | - Richard A. Schwarz
- Rice University, Department of Bioengineering MS-142, 6100 Main St., Houston, TX 77005, USA
| | - Curtis R. Pickering
- The University of Texas MD Anderson Cancer Center, Department of Head & Neck Surgery, 1400 Pressler Street, Houston, TX 77030, USA
| | - Michelle D. Williams
- The University of Texas MD Anderson Cancer Center, Department of Anatomical Pathology, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Nadarajah Vigneswaran
- The University of Texas Health School of Dentistry, Department of Diagnostic and Biomedical Sciences, 7500 Cambridge St., Houston, TX 77054, USA
| | - Ann M. Gillenwater
- The University of Texas MD Anderson Cancer Center, Department of Head & Neck Surgery, 1400 Pressler Street, Houston, TX 77030, USA
| | - Andrew G. Sikora
- The University of Texas MD Anderson Cancer Center, Department of Head & Neck Surgery, 1400 Pressler Street, Houston, TX 77030, USA
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Istasy P, Lee WS, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. J Med Internet Res 2022; 24:e39748. [PMID: 36005841 PMCID: PMC9667381 DOI: 10.2196/39748] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/11/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. OBJECTIVE We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?" METHODS Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. RESULTS Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. CONCLUSIONS Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.
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Affiliation(s)
- Paul Istasy
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Rotman Institute of Philosophy, Western University, London, ON, Canada
| | - Wen Shen Lee
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | | | - Ross Upshur
- Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Bridgepoint Collaboratory for Research and Innovation, Lunenfeld Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Bishal Gyawali
- Division of Cancer Care and Epidemiology, Department of Oncology, Queen's University, Kingston, ON, Canada
- Division of Cancer Care and Epidemiology, Department of Public Health Sciences, Queen's University, Kingston, ON, Canada
| | - Jacquelyn Burkell
- Faculty of Information and Media Studies, Western University, London, ON, Canada
| | - Bekim Sadikovic
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | - Alejandro Lazo-Langner
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Benjamin Chin-Yee
- Rotman Institute of Philosophy, Western University, London, ON, Canada
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Division of Hematology, Department of Medicine, London Health Sciences Centre, London, ON, Canada
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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10
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Brenes D, Barberan CJ, Hunt B, Parra SG, Salcedo MP, Possati-Resende JC, Cremer ML, Castle PE, Fregnani JHTG, Maza M, Schmeler KM, Baraniuk R, Richards-Kortum R. Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer. Comput Med Imaging Graph 2022; 97:102052. [PMID: 35299096 PMCID: PMC9250128 DOI: 10.1016/j.compmedimag.2022.102052] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 02/04/2022] [Accepted: 02/10/2022] [Indexed: 10/19/2022]
Abstract
Cervical cancer is a public health emergency in low- and middle-income countries where resource limitations hamper standard-of-care prevention strategies. The high-resolution endomicroscope (HRME) is a low-cost, point-of-care device with which care providers can image the nuclear morphology of cervical lesions. Here, we propose a deep learning framework to diagnose cervical intraepithelial neoplasia grade 2 or more severe from HRME images. The proposed multi-task convolutional neural network uses nuclear segmentation to learn a diagnostically relevant representation. Nuclear segmentation was trained via proxy labels to circumvent the need for expensive, manually annotated nuclear masks. A dataset of images from over 1600 patients was used to train, validate, and test our algorithm; data from 20% of patients were reserved for testing. An external evaluation set with images from 508 patients was used to further validate our findings. The proposed method consistently outperformed other state-of-the art architectures achieving a test per patient area under the receiver operating characteristic curve (AUC-ROC) of 0.87. Performance was comparable to expert colposcopy with a test sensitivity and specificity of 0.94 (p = 0.3) and 0.58 (p = 1.0), respectively. Patients with recurrent human papillomavirus (HPV) infections are at a higher risk of developing cervical cancer. Thus, we sought to incorporate HPV DNA test results as a feature to inform prediction. We found that incorporating patient HPV status improved test specificity to 0.71 at a sensitivity of 0.94.
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Affiliation(s)
| | | | - Brady Hunt
- Rice University, Houston, TX 77005, USA.
| | | | - Mila P Salcedo
- University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | | | | | | | | | - Mauricio Maza
- Basic Health International, San Savlador, El Salvador.
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11
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Li Q, Liu BR. Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer. Shijie Huaren Xiaohua Zazhi 2021; 29:1389-1395. [DOI: 10.11569/wcjd.v29.i24.1389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
In recent years, artificial intelligence (AI) combined with endoscopy has made an appearance in the diagnosis of early esophageal cancer (EC) and achieved satisfactory results. Due to the rapid progression and poor prognosis of EC, the early detection and diagnosis of EC are of great value for patient prognosis improvement. AI has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. In China, the detection of early EC depends on endoscopist expertise and is inevitably subject to interobserver variability. The excellent imaging recognition ability of AI is very suitable for the diagnosis and recognition of EC, thereby reducing the missed diagnosis and helping physicians to perform endoscopy better. This paper reviews the application and relevant progress of AI in the field of endoscopic detection of early EC (including squamous cell carcinoma and adenocarcinoma), with a focus on diagnostic performance of AI to identify different types of endoscopic images, such as sensitivity and specificity.
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Affiliation(s)
- Qing Li
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
| | - Bing-Rong Liu
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
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12
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Goyal H, Sherazi SAA, Mann R, Gandhi Z, Perisetti A, Aziz M, Chandan S, Kopel J, Tharian B, Sharma N, Thosani N. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021; 13:5494. [PMID: 34771658 PMCID: PMC8582733 DOI: 10.3390/cancers13215494] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.
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Affiliation(s)
- Hemant Goyal
- Department of Internal Medicine, The Wright Center for Graduate Medical Education, 501 S. Washington Avenue, Scranton, PA 18505, USA
| | - Syed A. A. Sherazi
- Department of Medicine, John H Stroger Jr Hospital of Cook County, 1950 W Polk St, Chicago, IL 60612, USA;
| | - Rupinder Mann
- Department of Medicine, Saint Agnes Medical Center, 1303 E. Herndon Ave, Fresno, CA 93720, USA;
| | - Zainab Gandhi
- Department of Medicine, Geisinger Wyoming Valley Medical Center, 1000 E Mountain Dr, Wilkes-Barre, PA 18711, USA;
| | - Abhilash Perisetti
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN 46845, USA; (A.P.); (N.S.)
| | - Muhammad Aziz
- Department of Gastroenterology and Hepatology, University of Toledo Medical Center, 3000 Arlington Avenue, Toledo, OH 43614, USA;
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, CHI Health Creighton University Medical Center, 7500 Mercy Rd, Omaha, NE 68124, USA;
| | - Jonathan Kopel
- Department of Medicine, Texas Tech University Health Sciences Center, 3601 4th St, Lubbock, TX 79430, USA;
| | - Benjamin Tharian
- Department of Gastroenterology and Hepatology, The University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR 72205, USA;
| | - Neil Sharma
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN 46845, USA; (A.P.); (N.S.)
| | - Nirav Thosani
- Division of Gastroenterology, Hepatology & Nutrition, McGovern Medical School, UTHealth, 6410 Fannin, St #1014, Houston, TX 77030, USA;
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13
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Tang Y, Anandasabapathy S, Richards‐Kortum R. Advances in optical gastrointestinal endoscopy: a technical review. Mol Oncol 2021; 15:2580-2599. [PMID: 32915503 PMCID: PMC8486567 DOI: 10.1002/1878-0261.12792] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/23/2020] [Accepted: 09/01/2020] [Indexed: 12/11/2022] Open
Abstract
Optical endoscopy is the primary diagnostic and therapeutic tool for management of gastrointestinal (GI) malignancies. Most GI neoplasms arise from precancerous lesions; thus, technical innovations to improve detection and diagnosis of precancerous lesions and early cancers play a pivotal role in improving outcomes. Over the last few decades, the field of GI endoscopy has witnessed enormous and focused efforts to develop and translate accurate, user-friendly, and minimally invasive optical imaging modalities. From a technical point of view, a wide range of novel optical techniques is now available to probe different aspects of light-tissue interaction at macroscopic and microscopic scales, complementing white light endoscopy. Most of these new modalities have been successfully validated and translated to routine clinical practice. Herein, we provide a technical review of the current status of existing and promising new optical endoscopic imaging technologies for GI cancer screening and surveillance. We summarize the underlying principles of light-tissue interaction, the imaging performance at different scales, and highlight what is known about clinical applicability and effectiveness. Furthermore, we discuss recent discovery and translation of novel molecular probes that have shown promise to augment endoscopists' ability to diagnose GI lesions with high specificity. We also review and discuss the role and potential clinical integration of artificial intelligence-based algorithms to provide decision support in real time. Finally, we provide perspectives on future technology development and its potential to transform endoscopic GI cancer detection and diagnosis.
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Affiliation(s)
- Yubo Tang
- Department of BioengineeringRice UniversityHoustonTXUSA
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14
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Hunt B, Fregnani JHTG, Brenes D, Schwarz RA, Salcedo MP, Possati-Resende JC, Antoniazzi M, de Oliveira Fonseca B, Santana IVV, de Macêdo Matsushita G, Castle PE, Schmeler KM, Richards-Kortum R. Cervical lesion assessment using real-time microendoscopy image analysis in Brazil: The CLARA study. Int J Cancer 2021; 149:431-441. [PMID: 33811763 PMCID: PMC8815862 DOI: 10.1002/ijc.33543] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/05/2021] [Accepted: 02/12/2021] [Indexed: 01/28/2023]
Abstract
We conducted a prospective evaluation of the diagnostic performance of high-resolution microendoscopy (HRME) to detect cervical intraepithelial neoplasia (CIN) in women with abnormal screening tests. Study participants underwent colposcopy, HRME and cervical biopsy. The prospective diagnostic performance of HRME using an automated morphologic image analysis algorithm was compared to that of colposcopy using histopathologic detection of CIN as the gold standard. To assess the potential to further improve performance of HRME image analysis, we also conducted a retrospective analysis assessing performance of a multi-task convolutional neural network to segment and classify HRME images. One thousand four hundred eighty-six subjects completed the study; 435 (29%) subjects had CIN Grade 2 or more severe (CIN2+) diagnosis. HRME with morphologic image analysis for detection of CIN Grade 3 or more severe diagnoses (CIN3+) was similarly sensitive (95.6% vs 96.2%, P = .81) and specific (56.6% vs 58.7%, P = .18) as colposcopy. HRME with morphologic image analysis for detection of CIN2+ was slightly less sensitive (91.7% vs 95.6%, P < .01) and specific (59.7% vs 63.4%, P = .02) than colposcopy. Images from 870 subjects were used to train a multi-task convolutional neural network-based algorithm and images from the remaining 616 were used to validate its performance. There were no significant differences in the sensitivity and specificity of HRME with neural network analysis vs colposcopy for detection of CIN2+ or CIN3+. Using a neural network-based algorithm, HRME has comparable sensitivity and specificity to colposcopy for detection of CIN2+. HRME could provide a low-cost, point-of-care alternative to colposcopy and biopsy in the prevention of cervical cancer.
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Affiliation(s)
- Brady Hunt
- Rice University, Department of Bioengineering, Houston, Texas
| | | | - David Brenes
- Rice University, Department of Bioengineering, Houston, Texas
| | | | - Mila P. Salcedo
- Federal University of Health Sciences of Porto Alegre (UFCSPA)/Santa Casa Hospital of Porto Alegre, Brazil
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD
| | | | | | | | | | | | - Philip E. Castle
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Rockville, MD
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD
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15
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Parra SG, López-Orellana LM, Molina Duque AR, Carns JL, Schwarz RA, Smith CA, Ortiz Silvestre M, Diaz Bazan S, Escobar PA, Felix JC, Ramalingam P, Castle PE, Cremer ML, Maza M, Schmeler KM, Richards-Kortum RR. Cervical cancer prevention in El Salvador: A prospective evaluation of screening and triage strategies incorporating high-resolution microendoscopy to detect cervical precancer. Int J Cancer 2021; 148:2571-2578. [PMID: 33368249 PMCID: PMC10568648 DOI: 10.1002/ijc.33454] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/16/2020] [Accepted: 11/27/2020] [Indexed: 01/28/2023]
Abstract
Cervical cancer remains a leading cause of cancer death for women in low- and middle-income countries. The goal of our study was to evaluate screening and triage strategies, including high-resolution microendoscopy (HRME), to detect cervical abnormalities concerning for precancer at the point of care. Women (n = 1824) were enrolled at the Instituto de Cáncer de El Salvador. All underwent screening by both human papillomavirus (HPV) testing using careHPV and visual inspection with acetic acid (VIA). Screen-positives, along with 10% of screen-negatives, were invited to return for a follow-up examination that included triage with VIA, colposcopy and HRME imaging. Biopsies were taken of any abnormalities identified. If no abnormalities were identified, then the worst scoring site by HRME was biopsied. The sensitivities of HPV testing and VIA to screen for cervical intraepithelial neoplasia Grade 2 or more severe diagnoses (CIN2+) were 82.1% and 75% (P = .77), while the specificities were 90.4% and 80.9% (P < .001), respectively. The sensitivities of VIA, colposcopy and HRME as triage tests for CIN2+ were 82.1%, 82.1% and 71.4%, respectively (P ≥ .38). HRME had a significantly higher specificity (66.7%) than VIA (51.9%) (P < .001) and colposcopy (53.3%) (P < .001). When evaluating different theoretical screening and triage strategies, screening with HPV testing followed by triage with HRME would result in more women receiving appropriate care (97%) compared to screening with VIA (75%) or HPV alone (90%). Our findings demonstrate that screening with HPV is superior to VIA, and that triage with HRME imaging increases the specificity of detecting CIN2+ at the point of care in a low-resource setting.
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Affiliation(s)
- Sonia G Parra
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | | | | | - Jennifer L Carns
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | | | - Chelsey A Smith
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | | | | | - Pablo A Escobar
- Liga Contra el Cáncer de El Salvador, San Salvador, El Salvador
| | - Juan C Felix
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Preetha Ramalingam
- Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Philip E Castle
- Divisions of Cancer Prevention and Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Miriam L Cremer
- Basic Health International, San Salvador, El Salvador
- Women's Health Institute, Department of Obstetrics and Gynecology, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mauricio Maza
- Basic Health International, San Salvador, El Salvador
| | - Kathleen M Schmeler
- Department of Gynecological Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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16
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Hunt B, Coole J, Brenes D, Kortum A, Mitbander R, Vohra I, Carns J, Schwarz R, Richards-Kortum R. High frame rate video mosaicking microendoscope to image large regions of intact tissue with subcellular resolution. BIOMEDICAL OPTICS EXPRESS 2021; 12:2800-2812. [PMID: 34123505 PMCID: PMC8176790 DOI: 10.1364/boe.425527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 03/26/2021] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
High-resolution microendoscopy (HRME) is a low-cost strategy to acquire images of intact tissue with subcellular resolution at frame rates ranging from 11 to 18 fps. Current HRME imaging strategies are limited by the small microendoscope field of view (∼0.5 mm2); multiple images must be acquired and reliably registered to assess large regions of clinical interest. Image mosaics have been assembled from co-registered frames of video acquired as a microendoscope is slowly moved across the tissue surface, but the slow frame rate of previous HRME systems made this approach impractical for acquiring quality mosaicked images from large regions of interest. Here, we present a novel video mosaicking microendoscope incorporating a high frame rate CMOS sensor and optical probe holder to enable high-speed, high quality interrogation of large tissue regions of interest. Microendoscopy videos acquired at >90 fps are assembled into an image mosaic. We assessed registration accuracy and image sharpness across the mosaic for images acquired with a handheld probe over a range of translational speeds. This high frame rate video mosaicking microendoscope enables in vivo probe translation at >15 millimeters per second while preserving high image quality and accurate mosaicking, increasing the size of the region of interest that can be interrogated at high resolution from 0.5 mm2 to >30 mm2. Real-time deployment of this high-frame rate system is demonstrated in vivo and source code made publicly available.
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Affiliation(s)
- Brady Hunt
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Jackson Coole
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - David Brenes
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Alex Kortum
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Ruchika Mitbander
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Imran Vohra
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Jennifer Carns
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Richard Schwarz
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
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17
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Liu Y. Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step? World J Gastroenterol 2021; 27:1392-1405. [PMID: 33911463 PMCID: PMC8047537 DOI: 10.3748/wjg.v27.i14.1392] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/23/2021] [Accepted: 03/13/2021] [Indexed: 02/06/2023] Open
Abstract
Esophageal cancer (EC) is a common malignant tumor of the digestive tract and originates from the epithelium of the esophageal mucosa. It has been confirmed that early EC lesions can be cured by endoscopic therapy, and the curative effect is equivalent to that of surgical operation. Upper gastrointestinal endoscopy is still the gold standard for EC diagnosis. The accuracy of endoscopic examination results largely depends on the professional level of the examiner. Artificial intelligence (AI) has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. This paper reviews the application of AI in the field of endoscopic detection of early EC, including squamous cell carcinoma and adenocarcinoma, and describes the relevant progress. Although up to now most of the studies evaluating the clinical application of AI in early EC endoscopic detection are focused on still images, AI-assisted real-time detection based on live-stream video may be the next step.
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Affiliation(s)
- Yong Liu
- Department of Thoracic Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430011, Hubei Province, China
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18
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Automated software-assisted diagnosis of esophageal squamous cell neoplasia using high-resolution microendoscopy. Gastrointest Endosc 2021; 93:831-838.e2. [PMID: 32682812 PMCID: PMC7855348 DOI: 10.1016/j.gie.2020.07.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 07/02/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS High-resolution microendoscopy (HRME) is an optical biopsy technology that provides subcellular imaging of esophageal mucosa but requires expert interpretation of these histopathology-like images. We compared endoscopists with an automated software algorithm for detection of esophageal squamous cell neoplasia (ESCN) and evaluated the endoscopists' accuracy with and without input from the software algorithm. METHODS Thirteen endoscopists (6 experts, 7 novices) were trained and tested on 218 post-hoc HRME images from 130 consecutive patients undergoing ESCN screening/surveillance. The automated software algorithm interpreted all images as neoplastic (high-grade dysplasia, ESCN) or non-neoplastic. All endoscopists provided their interpretation (neoplastic or non-neoplastic) and confidence level (high or low) without and with knowledge of the software overlay highlighting abnormal nuclei and software interpretation. The criterion standard was histopathology consensus diagnosis by 2 pathologists. RESULTS The endoscopists had a higher mean sensitivity (84.3%, standard deviation [SD] 8.0% vs 76.3%, P = .004), lower specificity (75.0%, SD 5.2% vs 85.3%, P < .001) but no significant difference in accuracy (81.1%, SD 5.2% vs 79.4%, P = .26) of ESCN detection compared with the automated software algorithm. With knowledge of the software algorithm, the specificity of the endoscopists increased significantly (75.0% to 80.1%, P = .002) but not the sensitivity (84.3% to 84.8%, P = .75) or accuracy (81.1% to 83.1%, P = .13). The increase in specificity was among novices (P = .008) but not experts (P = .11). CONCLUSIONS The software algorithm had lower sensitivity but higher specificity for ESCN detection than endoscopists. Using computer-assisted diagnosis, the endoscopists maintained high sensitivity while increasing their specificity and accuracy compared with their initial diagnosis. Automated HRME interpretation would facilitate widespread usage in resource-poor areas where this portable, low-cost technology is needed.
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19
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He Z, Wang P, Liang Y, Fu Z, Ye X. Clinically Available Optical Imaging Technologies in Endoscopic Lesion Detection: Current Status and Future Perspective. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:7594513. [PMID: 33628407 PMCID: PMC7886528 DOI: 10.1155/2021/7594513] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 01/13/2021] [Accepted: 01/27/2021] [Indexed: 01/02/2023]
Abstract
Endoscopic optical imaging technologies for the detection and evaluation of dysplasia and early cancer have made great strides in recent decades. With the capacity of in vivo early detection of subtle lesions, they allow modern endoscopists to provide accurate and effective optical diagnosis in real time. This review mainly analyzes the current status of clinically available endoscopic optical imaging techniques, with emphasis on the latest updates of existing techniques. We summarize current coverage of these technologies in major hospital departments such as gastroenterology, urology, gynecology, otolaryngology, pneumology, and laparoscopic surgery. In order to promote a broader understanding, we further cover the underlying principles of these technologies and analyze their performance. Moreover, we provide a brief overview of future perspectives in related technologies, such as computer-assisted diagnosis (CAD) algorithms dealing with exploring endoscopic video data. We believe all these efforts will benefit the healthcare of the community, help endoscopists improve the accuracy of diagnosis, and relieve patients' suffering.
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Affiliation(s)
- Zhongyu He
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Peng Wang
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Yuelong Liang
- Department of General Surgery, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou 310016, China
| | - Zuoming Fu
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Xuesong Ye
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
- State Key Laboratory of CAD and CG, Zhejiang University, Hangzhou 310058, China
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20
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Application of Artificial Intelligence in Gastrointestinal Endoscopy. J Clin Gastroenterol 2021; 55:110-120. [PMID: 32925304 DOI: 10.1097/mcg.0000000000001423] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/07/2020] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI), also known as computer-aided diagnosis, is a technology that enables machines to process information and functions at or above human level and has great potential in gastrointestinal endoscopy applications. At present, the research on medical image recognition usually adopts the deep-learning algorithm based on the convolutional neural network. AI has been used in gastrointestinal endoscopy including esophagogastroduodenoscopy, capsule endoscopy, colonoscopy, etc. AI can help endoscopic physicians improve the diagnosis rate of various lesions, reduce the rate of missed diagnosis, improve the quality of endoscopy, assess the severity of the disease, and improve the efficiency of endoscopy. The diversity, susceptibility, and imaging specificity of gastrointestinal endoscopic images are all difficulties and challenges on the road to intelligence. We need more large-scale, high-quality, multicenter prospective studies to explore the clinical applicability of AI, and ethical issues need to be taken into account.
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21
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Tang Y, Kortum A, Vohra I, Schwarz RA, Carns J, Kannady CR, Clavell-Hernandez J, Hu Z, Dhanani N, Richards-Kortum R. Initial Results of First In Vivo Imaging of Bladder Lesions Using a High-Resolution Confocal Microendoscope. J Endourol 2021; 35:1190-1197. [PMID: 33307957 DOI: 10.1089/end.2020.0757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
Purpose: Conventional cystoscopy plays an important role in detection of bladder cancer; however, it is difficult to differentiate benign and neoplastic lesions based on cystoscopic appearance alone. Advanced microscopic modalities, such as confocal laser endomicroscopy and optical coherence tomography, have been shown to provide critical histopathologic information to help identify neoplastic bladder lesions in real time, but their availability and clinical adoption are limited due to a high cost. In this study, we present the first use of a novel and low-cost ($ <5000) confocal high-resolution microendoscope (confocal HRME) for in vivo imaging of bladder lesions. Materials and Methods: In a cohort of 15 patients undergoing white light cystoscopy as part of their standard of care, high-resolution images of proflavine-stained bladder lesions were acquired in vivo using the confocal HRME. Based on these images, we evaluated the ability of the confocal HRME to visualize uroepithelium with subcellular resolution and high contrast. Furthermore, we analyzed the cellular architecture and staining patterns of benign and neoplastic bladder lesions in confocal HRME images and compared results to that of standard cystoscopy and histopathology. Results: In vivo imaging in the pilot study demonstrates that the confocal HRME resolved subcellular structures of bladder uroepithelium with high contrast. In a wide range of clinical conditions from normal bladder wall to benign and neoplastic lesions, confocal HRME images revealed important diagnostic features that correlated to histopathology. Conclusions: The confocal HRME provides an affordable, portable, and easy-to-use tool to allow real-time and high-contrast subcellular characterization of bladder lesions, well suited for bladder cancer detection in community and resource-constrained settings. The ClinicalTrials.gov Identifier: NCT02340650.
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Affiliation(s)
- Yubo Tang
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Alex Kortum
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Imran Vohra
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | | | - Jennifer Carns
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Christopher R Kannady
- Department of Surgery and University of Texas Health Science Center at Houston, Houston, Texas, USA
| | | | - Zhihong Hu
- Department of Pathology and Laboratory Medicine, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Nadeem Dhanani
- Department of Surgery and University of Texas Health Science Center at Houston, Houston, Texas, USA
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22
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Fu Z, Jin Z, Zhang C, He Z, Zha Z, Hu C, Gan T, Yan Q, Wang P, Ye X. The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology. IEEE ACCESS 2021; 9:41144-41167. [DOI: 10.1109/access.2021.3065104] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Hussein M, Everson M, Haidry R. Esophageal squamous dysplasia and cancer: Is artificial intelligence our best weapon? Best Pract Res Clin Gastroenterol 2020; 52-53:101723. [PMID: 34172257 DOI: 10.1016/j.bpg.2020.101723] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 02/07/2023]
Abstract
Esophageal cancer is the eight most common cancer in the world and is associated with a poor prognosis. Significant efforts are necessary to improve the detection of early squamous cell cancer such that curative endoscopic therapy can be offered. Studies have shown an overall miss rate of esophageal cancer of up to 6.4%. Human factors including fatigue and lack of attention may be a contributory factor. Computer aided detection and characterisation of early squamous cell cancer can be a second reader which potentially offsets these factors. Recent studies developing artificial intelligence systems show real promise in the detection of early squamous cell cancer and predicting depth of invasion to aid in the management of patients in the same endoscopic session. This has the potential to revolutionise this area of endoscopy.
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Affiliation(s)
- Mohamed Hussein
- Division of Surgery and Interventional Sciences, University College London, London, UK; Department of Gastroenterology, University College London Hospital, London, UK.
| | - Martin Everson
- Division of Surgery and Interventional Sciences, University College London, London, UK
| | - Rehan Haidry
- Department of Gastroenterology, University College London Hospital, London, UK
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24
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Sinonquel P, Bisschops R. Striving for quality improvement: can artificial intelligence help? Best Pract Res Clin Gastroenterol 2020; 52-53:101722. [PMID: 34172249 DOI: 10.1016/j.bpg.2020.101722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 12/22/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is of keen interest for global health development as potential support for current human shortcomings. Gastrointestinal (GI) endoscopy is an excellent substrate for AI, since it holds the genuine potential to improve quality in GI endoscopy and overall patient care by improving detection and diagnosis guiding the endoscopists in performing endoscopy to the highest quality standards. The possibility of large data acquisitioning to refine algorithms makes implementation of AI into daily practice a potential reality. With the start of a new era adopting deep learning, large amounts of data can easily be processed, resulting in better diagnostic performances. In the upper gastrointestinal tract, research currently focusses on the detection and characterization of neoplasia, including Barrett's, squamous cell and gastric carcinoma, with an increasing amount of AI studies demonstrating the potential and benefit of AI-augmented endoscopy. Deep learning applied to small bowel video capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. In the colon, multiple prospective trials including five randomized trials, showed a consistent improvement in polyp and adenoma detection rates, one of the main quality indicators in endoscopy. There are however potential additional roles for AI to assist in quality improvement of endoscopic procedures, training and therapeutic decision making. Further large-scale, multicenter validation trials are required before AI-augmented diagnostic gastrointestinal endoscopy can be integrated into our routine clinical practice.
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Affiliation(s)
- P Sinonquel
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - R Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.
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25
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Parasher G, Wong M, Rawat M. Evolving role of artificial intelligence in gastrointestinal endoscopy. World J Gastroenterol 2020; 26:7287-7298. [PMID: 33362384 PMCID: PMC7739161 DOI: 10.3748/wjg.v26.i46.7287] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/02/2020] [Accepted: 11/29/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.
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Affiliation(s)
- Gulshan Parasher
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
| | - Morgan Wong
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
| | - Manmeet Rawat
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
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Abstract
Traditional microscopy suffers from a fixed trade-off between depth-of-field (DOF) and spatial resolution—the higher the desired spatial resolution, the narrower the DOF. We present DeepDOF, a computational microscope that allows us to break free from this constraint and achieve >5× larger DOF while retaining cellular-resolution imaging—obviating the need for z-scanning and significantly reducing the time needed for imaging. The key ingredients that allow this advance are 1) an optimized phase mask placed at the microscope aperture; and 2) a deep-learning-based algorithm that turns sensor data into high-resolution, large-DOF images. DeepDOF offers an inexpensive means for fast and slide-free histology, suited for improving tissue sampling during intraoperative assessment and in resource-constrained settings. Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.
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Abstract
Artificial intelligence (AI) is now a trendy subject in clinical medicine and especially in gastrointestinal (GI) endoscopy. AI has the potential to improve the quality of GI endoscopy at all levels. It will compensate for humans' errors and limited capabilities by bringing more accuracy, consistency, and higher speed, making endoscopic procedures more efficient and of higher quality. AI showed great results in diagnostic and therapeutic endoscopy in all parts of the GI tract. More studies are still needed before the introduction of this new technology in our daily practice and clinical guidelines. Furthermore, ethical clearance and new legislations might be needed. In conclusion, the introduction of AI will be a big breakthrough in the field of GI endoscopy in the upcoming years. It has the potential to bring major improvements to GI endoscopy at all levels.
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Affiliation(s)
- Ahmad El Hajjar
- Department of Gastroenterology and Digestive Endoscopy, Arnault Tzanck Institute, Saint-Laurent du Var 06700, France
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Huang LM, Yang WJ, Huang ZY, Tang CW, Li J. Artificial intelligence technique in detection of early esophageal cancer. World J Gastroenterol 2020; 26:5959-5969. [PMID: 33132647 PMCID: PMC7584056 DOI: 10.3748/wjg.v26.i39.5959] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/22/2020] [Accepted: 09/04/2020] [Indexed: 02/06/2023] Open
Abstract
Due to the rapid progression and poor prognosis of esophageal cancer (EC), the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients. However, the endoscopic detection of early EC, especially Barrett's dysplasia or squamous epithelial dysplasia, is difficult. Therefore, the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence (AI). Deep learning (DL) has brought about breakthroughs in processing images, videos, and other aspects, whereas convolutional neural networks (CNNs) have shone lights on detection of endoscopic images and videos. Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from in vitro image analysis for classification to real-time detection of early esophageal neoplasia. When AI technique comes to the pathological diagnosis, borderline lesions that are difficult to determine may become easier than before. In gene diagnosis, due to the lack of tissue specificity of gene diagnostic markers, they can only be used as supplementary measures at present. In predicting the risk of cancer, there is still a lack of prospective clinical research to confirm the accuracy of the risk stratification model.
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Affiliation(s)
- Lu-Ming Huang
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Wen-Juan Yang
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zhi-Yin Huang
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Cheng-Wei Tang
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Jing Li
- Department of Gastroenterology, West China Hospital Sichuan University, Chengdu 610041, Sichuan Province, China
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Hussein M, González-Bueno Puyal J, Mountney P, Lovat LB, Haidry R. Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey. World J Gastroenterol 2020; 26:5784-5796. [PMID: 33132634 PMCID: PMC7579761 DOI: 10.3748/wjg.v26.i38.5784] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/12/2020] [Accepted: 09/12/2020] [Indexed: 02/06/2023] Open
Abstract
The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis. There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus. Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers, therefore curative endoscopic therapy can be offered. Research in this area of artificial intelligence is expanding and the future looks promising. In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development.
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Affiliation(s)
- Mohamed Hussein
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
| | - Juana González-Bueno Puyal
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom and Odin Vision, London W1W 7TS, United Kingdom
| | | | - Laurence B Lovat
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
| | - Rehan Haidry
- Department of GI Services, University College London Hospital, London NW1 2BU, United Kingdom
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Frontiers of Robotic Gastroscopy: A Comprehensive Review of Robotic Gastroscopes and Technologies. Cancers (Basel) 2020; 12:cancers12102775. [PMID: 32998213 PMCID: PMC7600666 DOI: 10.3390/cancers12102775] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/22/2020] [Accepted: 09/25/2020] [Indexed: 02/06/2023] Open
Abstract
Simple Summary With the rapid advancements of medical technologies and patients’ higher expectations for precision diagnostic and surgical outcomes, gastroscopy has been increasingly adopted for the detection and treatment of pathologies in the upper digestive tract. Correspondingly, robotic gastroscopes with advanced functionalities, e.g., disposable, dextrous and not invasive solutions, have been developed in the last years. This article extensively reviews these novel devices and describes their functionalities and performance. In addition, the implementation of artificial intelligence technology into robotic gastroscopes, combined with remote telehealth endoscopy services, are discussed. The aim of this paper is to provide a clear and comprehensive view of contemporary robotic gastroscopes and ancillary technologies to support medical practitioners in their future clinical practice but also to inspire and drive new engineering developments. Abstract Upper gastrointestinal (UGI) tract pathology is common worldwide. With recent advancements in robotics, innovative diagnostic and treatment devices have been developed and several translational attempts made. This review paper aims to provide a highly pictorial critical review of robotic gastroscopes, so that clinicians and researchers can obtain a swift and comprehensive overview of key technologies and challenges. Therefore, the paper presents robotic gastroscopes, either commercial or at a progressed technology readiness level. Among them, we show tethered and wireless gastroscopes, as well as devices aimed for UGI surgery. The technological features of these instruments, as well as their clinical adoption and performance, are described and compared. Although the existing endoscopic devices have thus far provided substantial improvements in the effectiveness of diagnosis and treatment, there are certain aspects that represent unwavering predicaments of the current gastroenterology practice. A detailed list includes difficulties and risks, such as transmission of communicable diseases (e.g., COVID-19) due to the doctor–patient proximity, unchanged learning curves, variable detection rates, procedure-related adverse events, endoscopists’ and nurses’ burnouts, limited human and/or material resources, and patients’ preferences to choose non-invasive options that further interfere with the successful implementation and adoption of routine screening. The combination of robotics and artificial intelligence, as well as remote telehealth endoscopy services, are also discussed, as viable solutions to improve existing platforms for diagnosis and treatment are emerging.
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Yang EC, Brenes DR, Vohra IS, Schwarz RA, Williams MD, Vigneswaran N, Gillenwater AM, Richards-Kortum RR. Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images. J Med Imaging (Bellingham) 2020; 7:054502. [PMID: 32999894 PMCID: PMC7503985 DOI: 10.1117/1.jmi.7.5.054502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 09/02/2020] [Indexed: 11/14/2022] Open
Abstract
Purpose: In vivo optical imaging technologies like high-resolution microendoscopy (HRME) can image nuclei of the oral epithelium. In principle, automated algorithms can then calculate nuclear features to distinguish neoplastic from benign tissue. However, images frequently contain regions without visible nuclei, due to biological and technical factors, decreasing the data available to and accuracy of image analysis algorithms. Approach: We developed the nuclear density-confidence interval (ND-CI) algorithm to determine if an HRME image contains sufficient nuclei for classification, or if a better image is required. The algorithm uses a convolutional neural network to exclude image regions without visible nuclei. Then the remaining regions are used to estimate a confidence interval (CI) for the number of abnormal nuclei per mm 2 , a feature used by a previously developed algorithm (called the ND algorithm), to classify images as benign or neoplastic. The range of the CI determines whether the ND-CI algorithm can classify an image with confidence, and if so, the predicted category. The ND and ND-CI algorithm were compared by calculating their positive predictive value (PPV) and negative predictive value (NPV) on 82 oral biopsies with histopathologically confirmed diagnoses. Results: After excluding the images that could not be classified with confidence, the ND-CI algorithm had higher PPV (65% versus 59%) and NPV (78% versus 75%) than the ND algorithm. Conclusions: The ND-CI algorithm could improve the real-time classification of HRME images of the oral epithelium by informing the user if an improved image is required for diagnosis.
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Affiliation(s)
- Eric C Yang
- Baylor College of Medicine, Houston, Texas, United States
| | - David R Brenes
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Imran S Vohra
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Richard A Schwarz
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Michelle D Williams
- The University of Texas, MD Anderson Cancer Center, Department of Pathology, Houston, Texas, United States
| | - Nadarajah Vigneswaran
- The University of Texas, School of Dentistry at Houston, Department of Diagnostic and Biomedical Sciences, Houston, Texas, United States
| | - Ann M Gillenwater
- The University of Texas, MD Anderson Cancer Center, Department of Head and Neck Surgery, Houston, Texas, United States
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32
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Zhang YH, Guo LJ, Yuan XL, Hu B. Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 2020; 26:5256-5271. [PMID: 32994686 PMCID: PMC7504247 DOI: 10.3748/wjg.v26.i35.5256] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/29/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett's esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert's performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.
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Affiliation(s)
- Yu-Hang Zhang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lin-Jie Guo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xiang-Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificia intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc 2020; 1:6-18. [DOI: 10.37126/aige.v1.i1.6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/07/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
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34
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Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificia intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc 2020. [DOI: 10.37126/wjem.v1.i1.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Lazăr DC, Avram MF, Faur AC, Goldiş A, Romoşan I, Tăban S, Cornianu M. The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future. MEDICINA (KAUNAS, LITHUANIA) 2020; 56:364. [PMID: 32708343 PMCID: PMC7404688 DOI: 10.3390/medicina56070364] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023]
Abstract
In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett's esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor-computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.
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Affiliation(s)
- Daniela Cornelia Lazăr
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Eftimie Murgu Sq. no. 2, 300041 Timișoara, Romania; (D.C.L.); (I.R.)
| | - Mihaela Flavia Avram
- Department of Surgery X, 1st Surgery Discipline, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Eftimie Murgu Sq. no. 2, 300041 Timișoara, Romania
| | - Alexandra Corina Faur
- Department I, Discipline of Anatomy and Embriology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Eftimie Murgu Sq. no. 2, 300041 Timișoara, Romania;
| | - Adrian Goldiş
- Department VII of Internal Medicine II, Discipline of Gastroenterology and Hepatology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Eftimie Murgu Sq. no. 2, 300041 Timișoara, Romania;
| | - Ioan Romoşan
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Eftimie Murgu Sq. no. 2, 300041 Timișoara, Romania; (D.C.L.); (I.R.)
| | - Sorina Tăban
- Department II of Microscopic Morphology, Discipline of Pathology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Eftimie Murgu Sq. no. 2, 300041 Timișoara, Romania; (S.T.); (M.C.)
| | - Mărioara Cornianu
- Department II of Microscopic Morphology, Discipline of Pathology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Eftimie Murgu Sq. no. 2, 300041 Timișoara, Romania; (S.T.); (M.C.)
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Gulati S, Patel M, Emmanuel A, Haji A, Hayee B, Neumann H. The future of endoscopy: Advances in endoscopic image innovations. Dig Endosc 2020; 32:512-522. [PMID: 31286574 DOI: 10.1111/den.13481] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 07/01/2019] [Indexed: 02/08/2023]
Abstract
The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.
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Affiliation(s)
- Shraddha Gulati
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Mehul Patel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Andrew Emmanuel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Amyn Haji
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Bu'Hussain Hayee
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Helmut Neumann
- Department of Medicine, University Hospital Mainz, Mainz, Germany
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37
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Hoogenboom SA, Bagci U, Wallace MB. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when? ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.tgie.2019.150634] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Parra S, Carranza E, Coole J, Hunt B, Smith C, Keahey P, Maza M, Schmeler K, Richards-Kortum R. Development of Low-Cost Point-of-Care Technologies for Cervical Cancer Prevention Based on a Single-Board Computer. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2020; 8:4300210. [PMID: 32190430 PMCID: PMC7062146 DOI: 10.1109/jtehm.2020.2970694] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 11/10/2019] [Accepted: 01/11/2020] [Indexed: 12/24/2022]
Abstract
Cervical cancer disproportionally affects women in low- and middle-income countries, in part due to the difficulty of implementing existing cervical cancer screening and diagnostic technologies in low-resource settings. Single-board computers offer a low-cost alternative to provide computational support for automated point-of-care technologies. Here we demonstrate two new devices for cervical cancer prevention that use a single-board computer: 1) a low-cost imaging system for real-time detection of cervical precancer and 2) a low-cost reader for real-time interpretation of lateral flow-based molecular tests to detect cervical cancer biomarkers. Using a Raspberry Pi computer to provide real-time image collection and processing, we developed: 1) a low-cost, portable high-resolution microendoscope system (PiHRME); and 2) a low-cost automatic lateral flow test reader (PiReader). The PiHRME acquired high-resolution ([Formula: see text]) images of the cervix at half the cost of existing high-resolution microendoscope systems; image analysis algorithms based on convolutional neural networks were implemented to provide real-time image interpretation. The PiReader acquired and analyzed images of a point-of-care human papillomavirus (HPV) serology test with the same contrast and accuracy as a standard flatbed high-resolution scanner coupled to a laptop computer, for less than one-fifth of the cost. Raspberry Pi single-board computers provide a low-cost means to implement point-of-care tools with automatic image analysis. This work demonstrates the promise of single-board computers to develop and translate low-cost, point-of-care technologies for use in low-resource settings.
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Affiliation(s)
- Sonia Parra
- Department of BioengineeringRice UniversityHoustonTX77005USA
| | | | - Jackson Coole
- Department of BioengineeringRice UniversityHoustonTX77005USA
| | - Brady Hunt
- Department of BioengineeringRice UniversityHoustonTX77005USA
| | - Chelsey Smith
- Department of BioengineeringRice UniversityHoustonTX77005USA
| | - Pelham Keahey
- Wellman Center for PhotomedicineHarvard Medical School and Massachusetts General HospitalBostonMA02114USA
| | - Mauricio Maza
- Basic Health International El SalvadorSan SalvadorCP1101El Salvador
| | - Kathleen Schmeler
- Department of Gynecologic Oncology and Reproductive MedicineThe University of Texas MD Anderson Cancer CenterHoustonTX77030USA
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Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B, Neumann H. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc 2020; 13:2631774520935220. [PMID: 32637935 PMCID: PMC7315657 DOI: 10.1177/2631774520935220] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 05/22/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence-augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence-augmented diagnostic luminal endoscopy into our routine clinical practice.
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Affiliation(s)
- Shraddha Gulati
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Andrew Emmanuel
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Mehul Patel
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Sophie Williams
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Amyn Haji
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Bu’Hussain Hayee
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Helmut Neumann
- Department of Interdisciplinary Endoscopy, University Hospital Mainz, 55131 Mainz, Germany
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Tang Y, Kortum A, Parra SG, Vohra I, Milbourne A, Ramalingam P, Toscano PA, Schmeler KM, Richards-Kortum RR. In vivo imaging of cervical precancer using a low-cost and easy-to-use confocal microendoscope. BIOMEDICAL OPTICS EXPRESS 2020; 11:269-280. [PMID: 32010516 PMCID: PMC6968771 DOI: 10.1364/boe.381064] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/09/2019] [Accepted: 12/11/2019] [Indexed: 05/08/2023]
Abstract
Cervical cancer incidence and mortality rates remain high in medically underserved areas. In this study, we present a low-cost (<$5,000), portable and user-friendly confocal microendoscope, and we report on its clinical use to image precancerous lesions in the cervix. The confocal microendoscope employs digital apertures on a digital light projector and a CMOS sensor to implement line-scanning confocal imaging. Leveraging its versatile programmability, we describe an automated aperture alignment algorithm to ensure clinical ease-of-use and to facilitate technology dissemination in low-resource settings. Imaging performance is then evaluated in ex vivo and in vivo pilot studies; results demonstrate that the confocal microendoscope can enhance visualization of nuclear morphology, contributing to significantly improved recognition of clinically important features for detection of cervical precancer.
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Affiliation(s)
- Yubo Tang
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
- The authors contributed equally to this work
| | - Alex Kortum
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
- The authors contributed equally to this work
| | - Sonia G. Parra
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Imran Vohra
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Andrea Milbourne
- The University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, TX 77057, USA
| | - Preetha Ramalingam
- The University of Texas MD Anderson Cancer Center, Department of Pathology, Houston, TX 77030, USA
| | - Paul A. Toscano
- The University of Texas Health Science Center at Houston, School of Public Health, Brownsville, TX 78520, USA
| | - Kathleen M. Schmeler
- The University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, TX 77057, USA
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Thakkar SJ, Kochhar GS. Artificial intelligence for real-time detection of early esophageal cancer: another set of eyes to better visualize. Gastrointest Endosc 2020; 91:52-54. [PMID: 31865996 DOI: 10.1016/j.gie.2019.09.036] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 09/29/2019] [Indexed: 02/08/2023]
Affiliation(s)
- Shyam J Thakkar
- Division of Gastroenterology, Hepatology, Nutrition, Allegheny Health Network, Pittsburgh, Pennsylvania, USA; Center for Advanced Endoscopy, Pittsburgh, Pennsylvania, USA
| | - Gursimran S Kochhar
- Division of Gastroenterology, Hepatology, Nutrition, Allegheny Health Network, Pittsburgh, Pennsylvania, USA; Center for Advanced Endoscopy, Pittsburgh, Pennsylvania, USA
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Mobile applications in oncology: A systematic review of health science databases. Int J Med Inform 2019; 133:104001. [PMID: 31706229 DOI: 10.1016/j.ijmedinf.2019.104001] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 06/21/2019] [Accepted: 10/01/2019] [Indexed: 02/07/2023]
Abstract
INTRODUCTION In recent years there has been an exponential growth in the number of mobile applications (apps) relating to the early diagnosis of cancer and prevention of side effects during cancer treatment. For health care professionals and users, it can thus be difficult to determine the most appropriate app for given needs and assess the level of scientific evidence supporting their use. Therefore, this review aims to examine the research studies that deal with this issue and determine the characteristics of the apps involved. METHODOLOGY This study involved a systematic review of the scientific literature on randomized clinical trials that use apps to improve cancer management among patients, using the Pubmed (Medline), Latin America and the Caribbean in Health Sciences (LILACS), and Cochrane databases. The search was limited to articles written in English and Spanish published in the last 10 years. A search of the App Store for iOS devices and Google Play for Android devices was performed to find the apps identified in the included research articles. RESULTS In total, 54 articles were found to analyze the development of an application in the field of oncology. These articles were most frequently related to the use of apps for the early detection of cancer (n = 28), particularly melanoma (n = 9). In total, 21 studies reflected the application used. The apps featured in nine articles were located using the App Store and Google Play (n = 9), of which five were created to manage cancer-related issues. The rest of the apps were designed for use in the general population (n = 4). CONCLUSIONS There is an increasing number of research articles that study the use of apps in the field of oncology; however, these mobile applications tend to disappear from app stores after the studies are completed.
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Yang EC, Vohra IS, Badaoui H, Schwarz RA, Cherry KD, Jacob J, Rodriguez J, Williams MD, Vigneswaran N, Gillenwater AM, Richards-Kortum RR. Prospective evaluation of oral premalignant lesions using a multimodal imaging system: a pilot study. Head Neck 2019; 42:171-179. [PMID: 31621979 PMCID: PMC7003735 DOI: 10.1002/hed.25978] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 09/03/2019] [Accepted: 09/17/2019] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Multimodal optical imaging, incorporating reflectance and fluorescence modalities, is a promising tool to detect oral premalignant lesions in real-time. METHODS Images were acquired from 171 sites in 66 patient visits for clinical evaluation of oral lesions. An automated algorithm was used to classify lesions as high- or low-risk for neoplasia. Biopsies were acquired at clinically indicated sites and those classified as high-risk by imaging, at the surgeon's discretion. RESULTS Twenty sites were biopsied based on clinical examination or imaging. Of these, 12 were indicated clinically and by imaging; 58% were moderate dysplasia or worse. Four biopsies were indicated by imaging evaluation only; 75% were moderate dysplasia or worse. Finally, four biopsies were indicated by clinical evaluation only; 75% were moderate dysplasia or worse. CONCLUSION Multimodal imaging identified more cases of high-grade dysplasia than clinical evaluation, and can improve detection of high grade precancer in patients with oral lesions.
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Affiliation(s)
- Eric C Yang
- MD/PhD Medical Scientist Training Program, Baylor College of Medicine, Houston, Texas, USA
| | - Imran S Vohra
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Hawraa Badaoui
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas
| | | | - Katelin D Cherry
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Justin Jacob
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas
| | - Jessica Rodriguez
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas
| | - Michelle D Williams
- Department of Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas
| | - Nadarajah Vigneswaran
- Department of Diagnostic and Biomedical Sciences, The University of Texas Health Science Center at Houston School of Dentistry, Houston, Texas
| | - Ann M Gillenwater
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, Texas
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Tang Y, Kortum A, Vohra I, Carns J, Anandasabapathy S, Richards-Kortum R. Simple differential digital confocal aperture to improve axial response of line-scanning confocal microendoscopes. OPTICS LETTERS 2019; 44:4519-4522. [PMID: 31517920 PMCID: PMC6959477 DOI: 10.1364/ol.44.004519] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 08/13/2019] [Indexed: 05/24/2023]
Abstract
Line-scanning confocal microendoscopy offers video-rate cellular imaging of scattering tissue with relatively simple hardware, but its axial response is inferior to that of point-scanning systems. Based on Fourier optics theory, we designed differential confocal apertures with a simple subtraction technique to improve the line-scanning sectioning performance. Taking advantage of digital slit apertures on a digital light projector and a CMOS rolling shutter, we demonstrate real-time optical sectioning performance comparable to point scanning in a dual-camera microendoscope (<$6,000). We validate the background rejection capability when imaging porcine columnar epithelium stained with fluorescent contrast agents with different uptake mechanisms and staining properties.
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Kundrod KA, Smith CA, Hunt B, Schwarz RA, Schmeler K, Richards-Kortum R. Advances in technologies for cervical cancer detection in low-resource settings. Expert Rev Mol Diagn 2019; 19:695-714. [PMID: 31368827 DOI: 10.1080/14737159.2019.1648213] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Cervical cancer mortality rates remain high in low- and middle-income countries (LMICs) and other medically underserved areas due to challenges with implementation and sustainability of routine screening, accurate diagnosis, and early treatment of preinvasive lesions. Areas covered: In this review, we first discuss the standard of care for cervical cancer screening and diagnosis in high- and low-resource settings, biomarkers that correlate to cervical precancer and cancer, and needs for new tests. We review technologies for screening and diagnosis with a focus on tests that are already in use in LMICs or have the potential to be adapted for use in LMICs. Finally, we provide perspectives on the next five years of technology development for improved cervical cancer screening and diagnosis in LMICs. Expert opinion: Innovation toward improved molecular and imaging tests is needed to enable effective, affordable see-and-treat approaches to detect and treat cervical precancer in a single visit. Current molecular tests remain too complex and/or costly for widespread use. Especially with imaging tests, decision support may improve performance of new technologies.
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Affiliation(s)
| | - Chelsey A Smith
- Department of Bioengineering, Rice University , Houston , TX , USA
| | - Brady Hunt
- Department of Bioengineering, Rice University , Houston , TX , USA
| | | | - Kathleen Schmeler
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center , Houston , TX , USA
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46
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Parra SG, Rodriguez AM, Cherry KD, Schwarz RA, Gowen RM, Guerra LB, Milbourne AM, Toscano PA, Fisher-Hoch SP, Schmeler KM, Richards-Kortum RR. Low-cost, high-resolution imaging for detecting cervical precancer in medically-underserved areas of Texas. Gynecol Oncol 2019; 154:558-564. [PMID: 31288949 DOI: 10.1016/j.ygyno.2019.06.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 06/18/2019] [Accepted: 06/24/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Cervical cancer rates in the United States have declined since the 1940's, however, cervical cancer incidence remains elevated in medically-underserved areas, especially in the Rio Grande Valley (RGV) along the Texas-Mexico border. High-resolution microendoscopy (HRME) is a low-cost, in vivo imaging technique that can identify high-grade precancerous cervical lesions (CIN2+) at the point-of-care. The goal of this study was to evaluate the performance of HRME in medically-underserved areas in Texas, comparing results to a tertiary academic medical center. METHODS HRME was evaluated in five different outpatient clinical settings, two in Houston and three in the RGV, with medical providers of varying skill and training. Colposcopy, followed by HRME imaging, was performed on eligible women. The sensitivity and specificity of traditional colposcopy and colposcopy followed by HRME to detect CIN2+ were compared and HRME image quality was evaluated. RESULTS 174 women (227 cervical sites) were included in the final analysis, with 12% (11% of cervical sites) diagnosed with CIN2+ on histopathology. On a per-site basis, a colposcopic impression of low-grade precancer or greater had a sensitivity of 84% and a specificity of 45% to detect CIN2+. While there was no significant difference in sensitivity (76%, p = 0.62), the specificity when using HRME was significantly higher than that of traditional colposcopy (56%, p = 0.01). There was no significant difference in HRME image quality between clinical sites (p = 0.77) or medical providers (p = 0.33). CONCLUSIONS HRME imaging increased the specificity for detecting CIN2+ when compared to traditional colposcopy. HRME image quality remained consistent across different clinical settings.
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Affiliation(s)
- Sonia G Parra
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | - Ana M Rodriguez
- The University of Texas Medical Branch, Department of Obstetrics & Gynecology, Galveston, TX 77555, USA
| | - Katelin D Cherry
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | - Richard A Schwarz
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | - Rose M Gowen
- Su Clinica Brownsville, Brownsville, TX 78526, USA
| | | | - Andrea M Milbourne
- The University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, TX 77057, USA
| | - Paul A Toscano
- The University of Texas Health Science Center at Houston, School of Public Health, Brownsville, TX 78520, USA
| | - Susan P Fisher-Hoch
- The University of Texas Health Science Center at Houston, School of Public Health, Brownsville, TX 78520, USA
| | - Kathleen M Schmeler
- The University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, TX 77057, USA
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Mori Y, Kudo SE, Mohmed HEN, Misawa M, Ogata N, Itoh H, Oda M, Mori K. Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective. Dig Endosc 2019; 31:378-388. [PMID: 30549317 DOI: 10.1111/den.13317] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 12/07/2018] [Indexed: 02/08/2023]
Abstract
With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early-stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false-negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real-time gastroscopy to provide on-site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.
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Affiliation(s)
- Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hussein E N Mohmed
- Showa University International Center for Endoscopy, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Department of Gastroenterology/Tropical Medicine, Ain Shams University, Cairo, Egypt
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Masahiro Oda
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Aichi, Japan
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Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019; 25:1666-1683. [PMID: 31011253 PMCID: PMC6465941 DOI: 10.3748/wjg.v25.i14.1666] [Citation(s) in RCA: 157] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 03/04/2019] [Accepted: 03/16/2019] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
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Affiliation(s)
- Young Joo Yang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Gangwon-do 24253, South Korea
| | - Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Gangwon-do 24253, South Korea
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A mobile-phone based high-resolution microendoscope to image cervical precancer. PLoS One 2019; 14:e0211045. [PMID: 30726252 PMCID: PMC6364962 DOI: 10.1371/journal.pone.0211045] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 01/07/2019] [Indexed: 01/12/2023] Open
Abstract
Nearly 90% of cervical cancer cases and deaths occur in low- and middle-income countries that lack comprehensive national HPV immunization and cervical cancer screening programs. In these settings, it is difficult to implement screening programs due to a lack of infrastructure and shortage of trained personnel. Screening programs based on visual inspection with acetic acid (VIA) have been successfully implemented in some low-resource settings. However, VIA has poor specificity and up to 90% of patients receiving treatment based on a positive VIA exam are over-treated. A number of studies have suggested that high-resolution cervical imaging to visualize nuclear morphology in vivo can improve specificity by better distinguishing precancerous and benign lesions. To enable high-resolution imaging in low-resource settings, we developed a portable, low-cost, high-resolution microendoscope that uses a mobile phone to detect and display images of cervical epithelium in vivo with subcellular resolution. The device was fabricated for less than $2,000 using commercially available optical components including filters, an LED and triplet lenses assembled in a 3D-printed opto-mechanical mount. We show that the mobile high-resolution microendoscope achieves similar resolution and signal-to-background ratio as previously reported high-resolution microendoscope systems using traditional cameras and computers to detect and display images. Finally, we demonstrate the ability of the mobile high-resolution microendoscope to image normal and precancerous squamous epithelium of the cervix in vivo in a gynecological referral clinic in Barretos, Brazil.
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50
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Yang EC, Vohra IS, Badaoui H, Schwarz RA, Cherry KD, Quang T, Jacob J, Lang A, Bass N, Rodriguez J, Williams MD, Vigneswaran N, Gillenwater AM, Richards-Kortum RR. Development of an integrated multimodal optical imaging system with real-time image analysis for the evaluation of oral premalignant lesions. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-10. [PMID: 30793567 PMCID: PMC6383051 DOI: 10.1117/1.jbo.24.2.025003] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 01/30/2019] [Indexed: 05/11/2023]
Abstract
Oral premalignant lesions (OPLs), such as leukoplakia, are at risk of malignant transformation to oral cancer. Clinicians can elect to biopsy OPLs and assess them for dysplasia, a marker of increased risk. However, it is challenging to decide which OPLs need a biopsy and to select a biopsy site. We developed a multimodal optical imaging system (MMIS) that fully integrates the acquisition, display, and analysis of macroscopic white-light (WL), autofluorescence (AF), and high-resolution microendoscopy (HRME) images to noninvasively evaluate OPLs. WL and AF images identify suspicious regions with high sensitivity, which are explored at higher resolution with the HRME to improve specificity. Key features include a heat map that delineates suspicious regions according to AF images, and real-time image analysis algorithms that predict pathologic diagnosis at imaged sites. Representative examples from ongoing studies of the MMIS demonstrate its ability to identify high-grade dysplasia in OPLs that are not clinically suspicious, and to avoid unnecessary biopsies of benign OPLs that are clinically suspicious. The MMIS successfully integrates optical imaging approaches (WL, AF, and HRME) at multiple scales for the noninvasive evaluation of OPLs.
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Affiliation(s)
- Eric C. Yang
- Rice University, Department of Bioengineering, Houston, Texas, United States
- Baylor College of Medicine, MD/PhD Medical Scientist Training Program, Houston, Texas, United States
| | - Imran S. Vohra
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Hawraa Badaoui
- University of Texas M.D. Anderson Cancer Center, Department of Head and Neck Surgery, Houston, Texas, United States
| | - Richard A. Schwarz
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Katelin D. Cherry
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Timothy Quang
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Justin Jacob
- University of Texas M.D. Anderson Cancer Center, Department of Head and Neck Surgery, Houston, Texas, United States
| | - Alex Lang
- University of Texas Health Science Center, School of Dentistry, Department of Diagnostic and Biomedical Sciences, Houston, Texas, United States
| | - Nancy Bass
- University of Texas Health Science Center, School of Dentistry, Department of Diagnostic and Biomedical Sciences, Houston, Texas, United States
| | - Jessica Rodriguez
- University of Texas M.D. Anderson Cancer Center, Department of Head and Neck Surgery, Houston, Texas, United States
| | - Michelle D. Williams
- University of Texas M.D. Anderson Cancer Center, Department of Pathology, Houston, Texas, United States
| | - Nadarajah Vigneswaran
- University of Texas Health Science Center, School of Dentistry, Department of Diagnostic and Biomedical Sciences, Houston, Texas, United States
| | - Ann M. Gillenwater
- University of Texas M.D. Anderson Cancer Center, Department of Head and Neck Surgery, Houston, Texas, United States
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